BUSAN205-23G (HAM)

Data Analytics with Business Applications

15 Points

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The University of Waikato
Academic Divisions
Division of Management
School of Accounting, Finance and Economics

Staff

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Convenor(s)

Lecturer(s)

Administrator(s)

: denise.martin@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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What this paper is about

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The exponential growth in the availability of data requires that students are able to make informed decisions using data, and effectively communicate their data analyses. This course covers the analytical and statistical techniques that business and management students are most likely to use in their future courses and professional careers. Students will learn different types of data analytics methods and their applications to problems in accounting, economics, finance, marketing, and business in general.

This course uses a combination of lectures, case discussions, lab sessions and student presentations. Students will have hands-on work with data and Microsoft Excel. Weekly computer-based workshops aim to enhance understanding of how the techniques introduced in lectures apply in a business context. Topics to be covered include presenting data using visual and descriptive statistics, measuring and understanding the relationship between variables, predictive analytics and prescriptive analytics tools. Empirical examples from economics, finance, accounting, marketing, supply chain and logistics will illustrate the material covered. Emphasis will be placed on understanding concepts and analysis of data. The paper will also provide opportunities for students to enhance their teamwork and communication skills with an empirical group research project and poster presentation.

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How this paper will be taught

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IMPORTANT NOTE: BUSAN205-23G(HAM) is NOT a Flexi paper, accordingly all of learning activities will be IN PERSON. There are significant assessments that only occur on-campus.

Two 3-hour in-class lectures and two 2-hour computer labs per week. Attendance at lectures and computer labs is strongly encouraged. Experience has shown that attending lectures and computer labs in person achieves the best outcomes for students. In lectures, we will carefully develop the basic ideas and tools, and provide some examples of the way they can be used. In labs, you will be given a set of questions and exercises to complete using Microsoft Excel.

In order to promote class participation and to provide immediate in-class feedback about specific concepts, we will use the Xorro-Q student response system. To participate, students will need an internet capable device (e.g. laptop, smartphone, tablet). The lecture theatres are all Wi-Fi enabled and there are no data charges for accessing the Xorro-Q website on campus. Instructions on how to use Xorro-Q will be provided on Moodle.

Please note that lab sessions commences in the first week of the trimester.

All students should attend two 2-hour lab session each week.

Labs are held after each lecture session as follows:

The first computer lab is either Wednesday afternoon or Thursday at few alternative times.

The second is on Friday at few alternative times.

Students may select a lab session by clicking on "Group Selection" in Moodle. Depending on enrollment numbers, and the availability of tutors, some tutorial times that are listed on the online timetable may not be available.

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Required Readings

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Camm, J., Cochran, J., Fry, M., Ohlmann, J., Anderson, D., Sweeney, D., and T. Williams (2020) Business Analytics, 4th edition, Cengage Learning. Earlier edition (3rd edition) of the book will also be suitable and it is available on course reserve at the library. This book is also available as an e-book from the University of Waikato library.

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You will need to have

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Recommended Readings

Koop, G (2013) Analysis of Economic Data, Wiley (on Course Reserve)

Duignan, J. (2014) Quantitative Methods for Business Research Using Microsoft Excel, Cengage Learning (On Course Reserve)

Hyndman, R. and Athanasopoulos, G. (2018) Forecasting: Principles and Practice, 2nd ed., OTexts: Melbourne, Australia (freely available online at https://otexts.com/fpp2)

Blastland, M. and Dilnot, A. (2010) The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics and in Life, Penguin Publishing Group

Cairo, A. (2019) How Charts Lie: Getting Smarter about Visual Information, WW Norton & Company

Tipoe, E. and Becker, R. (2020) Doing Economics: Empirical Projects (freely available online at http://www.coreecon.org/doing-economics/)

Harford, T. (2021) The Data Detective: Ten Easy Rules to Make Sense of Statistics, Penguin Publishing Group

If you have a spare hour, we highly recommend watching The Joy of Stats, featuring the late Hans Rosling. His enthusiasm for statistics is infectious and his graphic data visualizations are terrific. You can stream the video here: https://www.gapminder.org/videos/the-joy-of-stats/

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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Interpret business and economic data
    [LO 1]
    Linked to the following assessments:
  • Explain how data analytics theory applies to business decision making

    [LO 2]

    Linked to the following assessments:
  • Identify and apply appropriate data analytics methods to real world business issues and interpret the results, including analysis of random experiments and methods of comparing groups
    [LO 3]
    Linked to the following assessments:
  • Make inference on population means, difference between means for business decision making
    [LO 4]
    Linked to the following assessments:
  • Use regression analysis and critically appraise the merits and shortcomings of using regression methods to analyse empirical data
    [LO 5]
    Linked to the following assessments:
  • Evaluate evidence to inform decision making
    [LO 6]
    Linked to the following assessments:
  • Demonstrate proficiency in using Microsoft Excel as a statistical and analytical tool
    [LO 7]
    Linked to the following assessments:
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Assessments

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How you will be assessed

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Midterm Test
28 Nov 2023
2:00 PM
25
  • In Class: In Lecture
2. Lecture Participation & In-Class Surprise Quizzes
12
  • In Class: In Lecture
3. Computer Labs Attendance & Participation
10
  • In Class: In Lab
4. Final Test
13 Dec 2023
9:00 AM
33
  • In Class: In Lecture
5. Group Empirical Project and Presentation
15 Dec 2023
No set time
20
  • In Class: In Lab
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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